• Design and build Canvas and Model-Driven Apps using Power Apps • Develop Power Automate flows for process automation and system integration • Create and manage Power BI dashboards for data visualization and reporting • Implement Dataverse and other connectors for secure and efficient data storage • Customize and extend solutions using Power Fx, JavaScript, and APIs • Ensure solutions adhere to governance, security, and compliance standards • Troubleshoot and optimize existing Power Platform solutions • Collaborate with business users to gather requirements and translate them into technical designs • Stay current with Microsoft Power Platform updates and best practices

• Guides the Data Platform Engineers on highly complex projects related to fully operationalizing EMC's production data environments including alerting and monitoring • Tracks, reviews work in progress and completed assignments for projects within areas of expertise and follows up with team to ensure solutions created meet stakeholder needs and deadline for all technical delivery of project work per quarterly goals • Guides team during planning in selecting tasks needed to deliver data services based on priorities set by stakeholders and team roadmap • Leads planning and execution of strategic initiatives as requested by stakeholders and defined in DPO roadmap • Ensures delivery of assigned projects required for completion of DPO team enablement initiatives • Collaborates with Data Analytics Architecture Director to define and implement shared services solutions to support Data Analytics Product (DAP) teams • Leads the storage of data product files, enabling access as appropriate • Provides guidelines for access, use, and data retention, monitors growth and organization of assets • Creates and documents guidelines and procedures used by the DPO team members in the areas of Data Access and Security, Data Lifecycle and Change Management, Data Governance and Quality, and Data Masking • Focuses on efforts to shorten the systems development life cycle and provide continuous delivery, including statistical process controls, embedded data quality and governance, platform instrumentation, and enhanced self-service capabilities • Ensures proper implementation, education, and adherence to current guidelines and procedures

• Designs and evolves the data platform using Snowflake, DBT, Sigma, and AWS (Glue, Lambda, SageMaker), ensuring performance, modularity, and scalability for analytics and AI workloads. • Creates and communicates clear architecture direction for software and product team services, which build upon and improve operational effectiveness and efficiencies. • Designs infrastructure and workflows that support machine learning and generative AI use cases, including semantic modeling, feature engineering, and model deployment pipelines. • Identifies trends in technology and anticipates new requirements; recommends and supports strategies to leverage emerging and innovative technologies. • Builds and maintains sandbox environments for experimentation with predictive models, LLMs, and real-time analytics using Snowflake ML and AWS AI services. • Guides integration of machine learning and generative AI into data pipelines and analytics workflows, enabling use cases like semantic search, predictive modeling, and intelligent automation. • Recommends and presents technology applications strategy to meet the current and future needs of respective business areas. • Ensures foundational patterns (e.g., DBT macros, ML preprocessing frameworks) align with enterprise architecture and support AI/ML scalability, governance, and compliance. • Recommends and supports the technical strategy and alignment of data platform capabilities with the business objectives of EMC. • Partners with Data Platform Operations, Information Security, Network, Cloud Engineering, and DevOps teams to lead the design and implementation of new business initiatives as related to data and analytics. • Partners with Platform Operations, Analytics Engineering, and DAP teams to embed AI-ready data platform(s) into data products and ensure alignment with business outcomes. • Serves as a subject matter expert in data analytics platform(s) and AI/ML strategy, influencing platform innovation and guiding teams on best practices for scalable, intelligent data systems. • Serves as a technical resource for Data Platform team members on complex questions or issues. • Designs and recommends software engineering governance policies, procedures, and roles, including proper development techniques and quality management following established SDLC procedures. • Partners with Information Security to create and maintain security and procedures aligned and documented to risk management requirements. • Partners with Enterprise Architecture team to incorporate the creation of patterns and services into reference architecture and SDLC. • Provides guidance for building and maturing assigned solution platforms through automation and reusable processes. • Recommends and supports patterns that guide engineering team members in creating data solutions. • Enhances enterprise engineering practices by sharing expertise in creating engineering processes, alerting, and metrics. • Performs other duties as assigned.

• Architect, design, and develop platform services with a strong focus on scalability, security, and developer experience. • Lead operational design for reliability: build comprehensive observability, monitoring, and incident response automation into security-critical services. • Build automation and tooling to drive self-healing systems, proactive risk detection, failure recovery, and continuous resilience testing. • Collaborate with compliance, governance, and risk teams to translate regulatory and policy requirements into scalable technical controls. • Lead technical design reviews, security architecture reviews, and incident postmortems for platform-level incidents. • Mentor engineers across multiple disciplines on both security and operational best practices. • Own end-to-end delivery of services: from initial design and development through deployment, production hardening, and lifecycle maintenance.